Predictive location selection transportation optimization system

ABSTRACT

A method and system for predictive location selection are described. A network computer system can preselect a service area, prior to receiving a service request from a user, based on the position of the user, a destination, and proximate available service providers, among other factors. In response to the user inputting the destination, the network computer system determines probability scores for predefined service areas based on likelihoods of the proximate available service providers being available at a time when the user submits a service request. The network computer system uses the probability scores to select an appropriate service area and transmits data corresponding to the optimal service area to the user.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.15/600,570, filed May 19, 2017; the foregoing priority application beinghereby incorporated by reference in its entirety.

BACKGROUND

A network computer system can enable users to request and receivevarious services through applications executed on mobile computingdevices. The network computer system typically selects a serviceprovider to fulfill requests based on user-specific data from therequest, such as the location of the user, and provider-specific data,such as the locations of nearby providers. These service providers caninteract with the network computer system to accept or decline servicerequests, receive data about the requesting users, and set variousstatus modes such as whether the provider is offline or online andavailable to fulfill requests.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example network computersystem in communication with service requester devices and serviceprovider devices, in accordance with examples described herein.

FIG. 2 is a block diagram illustrating an example map scenario, inaccordance with examples described herein.

FIG. 3 is a block diagram illustrating an example on-demand networkservice with predictive location selection, in accordance with examplesdescribed herein.

FIG. 4 is a flow chart describing an example method of operating anetwork computer system with predictive location selection, according toexamples described herein.

FIG. 5 is a flow chart describing an example method of predictivelocation selection, according to examples described herein.

FIG. 6 is a block diagram illustrating an example service requesterdevice executing a service requester application for an on-demandservice, as described herein.

FIG. 7 is a block diagram that illustrates a computer system upon whichaspects described herein may be implemented.

DETAILED DESCRIPTION

A network computer system is provided herein that manages an on-demandnetwork-based service linking available service providers with servicerequesters throughout a given region (e.g., a metroplex such as the SanFrancisco Bay Area). According to examples, the network computer systemcan receive service requests for on-demand services (e.g., transportservice or delivery service) from requesting users (e.g., riders) via adesignated service requester application executing on the users' mobilecomputing devices. Based, at least in part, on the user's position, thenetwork computer system can identify a number of proximate availableservice providers (e.g., drivers) and transmit a service invitationmessage to one or more service provider devices of the proximateavailable service providers to fulfill the service request (e.g.,provide or perform the corresponding service). In many examples, theservice providers can either accept or decline the invitation based on,among other reasons, a service location or service destination beingimpractical for the service provider.

In some systems, users can manually choose the service location or usetheir current location as the service location. This manual choice canoften result in locations that are unsuited for actual road conditions,which may delay pick up time or total trip time. Users may also need tocontact the provider to adjust the service location, for example tocross the street, or because the initially-proposed service location issuboptimal or not suitable for the pickup. In addition, the manualselection of a service location can add an additional step in the user'srequest for service.

In some aspects, the network computer system can select the servicelocation based on the position of the user and proximate availableservice providers. The network computer system can also select theservice location prior to receiving a service request from the user. Inone example, in response to the user inputting a destination for atransportation service, the network computer system determines thespecific service location or a more general service area (e.g., anintersection of roads), which may be where the user has the highestprobability of matching with a service provider, or the highest matchquality, upon submitting a service request to the network computersystem. In another example, the network computer system can determinethe highest match quality between the service provider and multipleusers.

In conventional service requester applications used with on-demandservice systems, once a user makes a request for service (e.g., therequest is sent to the network computer system), the user waits at a“requesting” screen while the network computer system performs aselection operation to identify a service provider from one or morecandidate service providers. The requesting screen typically transitionsto a “service in progress” screen with the service provider's specificinformation once that service provider has accepted the invitation toprovide the service.

In some examples, to avoid having the user wait at a “requesting” screenwhile the network computer system determines a service location and acorresponding service provider, the network computer system canpreselect a service location for the user, prior to the user making therequest for service, where the user is likely able to rendezvous with aservice provider. The network computer system can also transmitdirections to the service location to the user's computing device. Toprevent the preselected service location from going stale, the user'scomputing device can request an updated service location from thenetwork computer system at regular intervals (e.g., 30 seconds).

In one aspect, the network computer system can select the servicelocation based on a best match location or best match service providerupon receiving the location of the user or receiving a destination fromthe user. However, if the user delays making a request for service, evenif only for ten seconds, the preselected best match may no longer be thebest match once the user makes the request. For example, the bestservice provider may be traveling quickly or in the opposite directionfrom the user, thus making that service provider a poor choice, or eveninfeasible, after ten seconds. In high traffic cities with one-waystreets and avenues, it can take a provider a significant amount of timeto loop back around to a pickup location behind the service provider'scurrent location. This can result in user cancellations, providercancellations, low efficiency, and overall poor service.

Therefore, rather than selecting the service location based on a currentbest match provider or location, the network computer system can takeinto account inherent delays in the system and the process of a usersubmitting a service request. As such, the network computer system canselect a service location or area where the user has the highestprobability of matching, or highest expected match quality, with aservice provider at a future time (i.e., a time when the user ispredicted to submit a service request to the network computer system).

According to examples described herein, the network computer systemreceives data corresponding to the position of the user from anapplication running on a computing device of the user. The networkcomputer system locates service providers based on the position of theuser (e.g., service providers within a threshold distance or travel timefrom the position of the user). In other examples, the network computersystem locates the service providers in response to receiving adestination for a transportation service from the user. In addition, thenetwork computer system selects predefined service areas, such as setsof corners at intersections, within a threshold distance or travel timefrom the position of the user.

In some aspects, the network computer system determines a probabilityscore for at least some of the predefined service areas based onpredicted availability of the service providers to provide service tothe user. The predicted availability is determined based on a time whenthe user is expected to request service. In some examples, this time ispredetermined, such as fifteen seconds from the current time. In otherexamples, the network computer system can predict when the user isexpected to request service, which can be based on historical data forthe user and other users.

Determining the predicted availability of a given provider within agiven predefined service area is based on an aggregate of factors. Amongothers, these factors can include travel speed for the given provider,distance of the given provider to the given predefined service area, adirectional heading of the given provider, a service destinationspecified by the user, a deviation between the directional heading forthe given provider and a heading towards the service destination, numberof people in a vehicle driven by the given provider, a prediction thatthe given provider receives another service request before the userrequests service, and historical availability data for the givenpredefined service area.

In some aspects, the network computer system selects an optimal one ofthe predefined service areas based at least on the probability scores,and transmits, to the computing device of the user, data correspondingto the optimal service area to be displayed on the computing device inresponse to the user submitting the request for service. The data caninclude a map location for the optimal service area and walkingdirections from the position of the user to the optimal service area.

In some aspects, the network computer system receives data correspondingto the request for service from the computing device of the user andselects an available provider at that time to provide service for theuser at a point within the selected service area. For example, theselected service area can be an intersection and the point within theselected service area is one of the corners of the intersection.

Upon making the request, the user's computing device can display thedirections to the service location while the network computer systemidentifies a service provider from the candidate service providers toprovide service at the preselected service location.

In some examples, in selecting a service provider to fulfill a givenservice request, the network computer system can identify candidateservice providers to fulfill the service request within the service area(e.g., at one of the corners of the intersection) that was identifiedand transmitted to the user prior to the service request. For example,the network computer system can determine a geo-fence (e.g., a regionspecified by three or more location points or a defined area, such as ahexagon from an array of hexagons) surrounding the service area (or ageo-fence defined by a radius away from the service area), identify aset of candidate service providers (e.g., twenty or thirty serviceproviders within the geo-fence), and select a service provider (e.g.,the closest service provider to the service area, service provider withthe shortest estimated travel time to the service area, service providertraveling to a location within a specified distance or specified traveltime to the destination location, etc.) from the set of candidateservice providers to fulfill the service request. According to examplesprovided herein, the network computer system can compile historical datafor individual service requesters with regard to the network-basedservice. Thus, the network computer system can manage a profile for eachuser indicating routine start and/or end locations (or regions), and/orroutine routes (e.g., for a transportation service from home to workand/or vice versa) and preferred service types (e.g., transportation,delivery, mailing, etc.). In some examples, the network computer systemcan further synchronize with a service requester device to, for example,identify the service requester's contacts, the service requester'sschedule and appointments, travel plans (e.g., a scheduled trip), andthe like.

After selecting an available provider and/or receiving confirmation fromthe selected provider, the network computer system can transmit datacorresponding to the selected provider and the point within the selectedservice area to the computing device of the user to be displayed.

Among other benefits, the examples described herein achieve a technicaleffect of providing walking directions to a user after the user submitsa service request, without delaying to select a service provider ordetermine an exact service location.

As provided herein, the terms “user” and “service requester” are usedthroughout this application interchangeably to describe a person orgroup of people who utilize a service requester application on acomputing device to request, over one or more networks, on-demandservices from a network computer system. The term “service provider” isused to describe a person utilizing a service provider application on acomputing device to provide on-demand services to the servicerequesters.

One or more aspects described herein provide that methods, techniques,and actions performed by a computing device are performedprogrammatically or as a computer-implemented method. Programmaticallymeans through the use of code or computer-executable instructions. Aprogrammatically performed step may or may not be automatic.

One or more aspects described herein may be implemented usingprogrammatic modules or components. A programmatic module or componentmay include a program, a subroutine, a portion of a program, a softwarecomponent, or a hardware component capable of performing one or morestated tasks or functions. In addition, a module or component can existon a hardware component independently of other modules or components.Alternatively, a module or component can be a shared element or processof other modules, programs, or machines.

Furthermore, one or more aspects described herein may be implementedthrough the use of instructions that are executable by one or moreprocessors. These instructions may be carried on a computer-readablemedium. Machines shown or described with figures below provide examplesof processing resources and computer-readable media on whichinstructions for implementing some aspects can be carried out orexecuted. In particular, the numerous machines shown in some examplesinclude processors and various forms of memory for holding data andinstructions. Examples of computer-readable media include permanentmemory storage devices, such as hard drives on personal computers orservers. Other examples of computer storage media include portablestorage units, such as CD or DVD units, flash or solid state memory(such as carried on many cell phones and consumer electronic devices)and magnetic memory. Computers, terminals, network-enabled devices(e.g., mobile devices such as cell phones) are all examples of machinesand devices that utilize processors, memory, and instructions stored oncomputer-readable media.

Alternatively, one or more examples described herein may be implementedthrough the use of dedicated hardware logic circuits that are comprisedof interconnected logic gates. Such circuits are typically designedusing a hardware description language (HDL), such as Verilog or VHDL.These languages contain instructions that ultimately define the layoutof the circuit. However, once the circuit is fabricated, there are noinstructions, and the processing is performed by interconnected logicgates.

System Overview

FIG. 1 is a block diagram illustrating an example network computersystem in communication with service requester and service providerdevices, in accordance with examples described herein. The networkcomputer system 100 can implement or manage a network service (e.g., anon-demand transport or delivery arrangement service) that connectsservice requesters with service providers that are available to fulfillthe service requests at a service area or location.

A request processing server 130 managing the network service can enableservice requesters to submit requests over one or more networks 150through a service requester application executing on the servicerequester devices 110. In some aspects, a location selector 132 providedwith the request processing server 130 can determine a suitable servicelocation based on the position of the user and proximate availableservice providers. The location selector 132 can also select the servicelocation prior to receiving a service request from the user. In oneaspect, in response to the user inputting a destination for atransportation service, the location selector 132 determines thespecific service location or a more general service area (e.g., anintersection of roads), which may be where the user has the highestprobability of matching with a service provider upon submitting aservice request to the request processing server 130.

Upon receiving service requests, a provider selector 134 of the requestprocessing server 130 processes and transmits the requests toappropriate service providers by way of a service provider applicationexecuting on the service provider devices 120. As used herein, a servicerequester device 110 and a service provider device 120 can comprisecomputing devices with functionality to execute designated applicationscorresponding to the network service managed by the network computersystem 100. In many examples, service requester devices 110 and serviceprovider devices 120 can comprise mobile computing devices, such assmartphones, tablet computers, virtual reality or augmented realityheadsets, on-board computing systems of vehicles, and the like. Examplenetwork services can comprise delivery of food or products, packagemailing, shopping, construction, plumbing, home repair, housing orapartment sharing, etc., or can include transportation arrangementservices. In an example using transport arrangement services, theservice requesters are prospective passengers to be picked up andtransported, and the service providers are drivers who transport theservice requesters.

According to examples, service interfaces on the request processingserver 130 enable the network computer system 100 to exchange data withthe service requester devices 110 and the service provider devices 120over the network 150. For example, the service interfaces can use one ormore network resources to exchange communications over one or morewireless networks (e.g., a cellular transceiver, a WLAN transceiver,etc.). The service interfaces can include or implement anexternally-facing application programming interface (API) to communicatedata with the service requester devices 110 and the service providerdevices 120. The externally-facing API can provide access to the requestprocessing server 130 via secure channels over the network 150 throughany number of methods, including web-based forms, programmatic accessvia restful APIs, Simple Object Access Protocol (SOAP), remote procedurecall (RPC), scripting access, etc.

In some examples, the location selector 132 stores data for predefinedservice areas and service locations in one or more database systems,such as location datastore 142. The provider selector 134 can storeprovider data, including provider profiles and current statusinformation, in one or more database systems, such as provider datastore144.

FIG. 2 is a block diagram illustrating an example map scenario, inaccordance with examples described herein. In some situations, it ismore efficient for the request processing server 130 to select alocation near a user's current position to designate as a pickuplocation for an on-demand transportation service, rather than having theuser 210 manually choose the pickup location. The request processingserver 130 receives any destination information and the current positionof the user 210 and retrieves data corresponding to nearby drivers201-209, prior to the user 210 submitting a service request to thenetwork computer system 100. Based on the positions, speed, and headingsof the drivers 201-209, the location selector 132 can determine likelyavailability of each driver at a number of predefined service areas nearthe position of the user 210 (e.g., intersections A-F). The locationselector 132 scores each intersection for each driver and aggregates thedriver scores to select one of the predefined service areas as anoptimal area to use as a pickup location once the user 210 submits aservice request.

According to some examples, the user 210 can indicate to the networkcomputer system 100 that the user 210 is willing to travel to an areathat a location selector 132 suggests as a good pickup location. Theuser 210 may indicate specific preferences (e.g., a willingness to walka maximum distance, such as two city blocks or willingness to incur moreexpense by having a shorter walking distance to the user's destination),by opting in or agreeing to an option via a user interface of theservice requester application. In the example of FIG. 2, intersectionsA-F represent the predefined service areas within the specificpreferences (e.g., maximum walking distance) the user 210 set.

In one aspect, the request processing server 130 can select the pickuplocation based on a best match location or best match driver. However,if the user 210 delays making a request for service, even if only forten seconds, the selected best match may no longer be the optimal choiceonce the user 210 makes the request. For example, a driver may betraveling quickly or in the opposite direction from the user 210, thusmaking that driver a poor match for the user 210 after ten seconds. Inhigh traffic cities with one-way streets and avenues, it can take adriver a significant amount of time to loop back around to a pickuplocation that the driver has already passed. This can result in usercancellations, driver cancellations, low efficiency, and overall poorservice.

Therefore, rather than selecting the service location based on a currentbest match provider or location, the location selector 132 can select aservice location or area where the user 210 has the highest probabilityof matching with a service provider at a future time (i.e., a time whenthe user 210 is predicted to submit a service request to the networkcomputer system 100). That is, of intersections A-F, which intersectionshould the user 210 walk towards to get a ride to the destination.

In the example of FIG. 2, the user 210 opens a service application thatcommunicates the position of the user 210 to the network computer system100. The user 210 can input a destination through the user interface ofthe service application. Based on factors such as the position of theuser 210, the destination, and current supply provisioning of drivers201-209, the location selector 132 can determine which of the nearbyintersections A-F to designate as the service area for a service requestthat the user 210 may submit to the network computer system 100.

In some aspects, the location selector 132 can determine which of theintersections A-F to select as the service area based on base scores andaggregate probabilities for the availability of each of the drivers201-209 at each of the intersections. The location selector 132 can basethe probability of a driver being available at an intersection on thetravel speed for the driver, distance of the driver to the intersection,a directional heading of the driver, the destination specified by theuser 210, a deviation between the directional heading for the driver anda heading towards the destination, a number of people in the driver'svehicle (i.e., other passengers in a ride-pooling service), a predictionthat the driver receives another service request before the user 210requests service, historical or real-time traffic conditions around theservice area, distance the driver must backtrack to get to the servicearea, historical availability data for a region around the intersection,among other factors associated with the driver, service area, and user.

In the example illustrated, intersection A is the closest of thepredefined service areas to the position of the user 210. In addition,four vehicles belonging to drivers 201, 202, 203, and 205 are nearintersection A. However, since the user 210 has not yet submitted aservice request and may not do so for another fifteen seconds, thelocation selector 132 takes into account the probabilities that each ofthose drivers may be available at intersection A in fifteen seconds.

In this example, drivers 203 and 205 have already passed intersection Aand can therefore be assigned very low probabilities of being availableat intersection A. Drivers 201 and 202 are approaching intersection A,and may or may not be available depending on their speed, distance, andtraffic conditions (including traffic signals, stop signs, etc.). Forexample, if driver 201 has just stopped at a red light and intends to gostraight, the driver has a high probability of being available atintersection A in fifteen seconds. On the other hand, if driver 202 hasa green light and is traveling at 30 miles per hour, then driver 202 hasa low probability of being available at intersection A in fifteenseconds. Based on their combined probabilities, the location selector132 can calculate a probability score for intersection A that representsthe overall probability of at least one of the drivers 201-209 beingavailable at intersection A in fifteen seconds.

In some aspects, the location selector 132 can determine the probabilityof a driver being available at an intersection based on a directionalheading of the driver, the destination specified by the user 210, and adeviation between the directional heading for the driver and a headingtowards the destination. For example, when considering the availabilityof driver 202, the location selector 132 can assign a higher probabilityof driver 202 going straight to intersection D than turning towardsintersection B. In another example, when considering the availability ofdrivers 208 and 209, the location selector 132 can assign them a lowerprobability of being available to take user 210 to the destinationbecause the drivers' headings are in the opposite direction from thedestination. Driver 204, on the other hand, is heading straight towardsthe destination and therefore may be assigned a higher probability ofbeing available (e.g., at intersections C and F).

In some aspects, the location selector 132 can determine the probabilityof a driver being available at an intersection based on a number ofpeople in the driver's vehicle (i.e., other passengers in a ride-poolingservice). For example, if the driver has a passenger in the vehicle,there is an increased probability that the driver continues on thecurrent heading. Additional passengers can increase this probabilitybecause of a higher likelihood that the passengers' destinations arecloser together and located in the same general direction as the currentheading. Therefore, for each additional passenger in a vehicle, thelocation selector 132 can assign higher probabilities to the driver ofbeing available for the user 210 at an intersection.

In further aspects, the location selector 132 can determine theprobability of a driver being available at an intersection based on aprediction that the driver receives another service request before theuser 210 requests service. The location selector 132 can retrieve supplyprovisioning information such as the number of local drivers, servicerequesters, average idle times, etc. to determine the probability that agiven driver may receive and accept a service request from a differentprospective passenger prior to the user 210 submitting a servicerequest.

In further aspects, the location selector 132 can determine theprobability of a driver being available at an intersection based onhistorical availability data for an area around the intersection.Certain areas are poorly suited for picking up passengers due to hightraffic, crowds, street layouts, etc. (e.g., popular tourist spots orsubway stations). The location selector 132 can take advantage ofhistorically-compiled map and pickup data identifying which areas of acity are more challenging for drivers to pick up passengers. In oneexample, the map data divides the city into grids and assignsprobability weights to each of the grid points based on the difficultyof using a pickup location in that area.

In one aspect, the location selector 132 selects the intersection withthe highest probability score. In other aspects, the location selector132 calculates or receives a base score that indicates the quality of amatch between the driver and the user. The location selector 132 canthen modify the base score for each driver by their respectiveprobability score to obtain a net score. In one aspect, the net score isthe product of the base score and probability score. The locationselector 132 can use the net scores, rather than the base scores orprobability scores alone, to select the intersection for the user. Inone example, the location selector 132 selects the intersectioncorresponding to the highest individual net score among the drivers.

Once the intersection is selected, the location selector 132 transmits,to the computing device of the user 210, data corresponding to theselected intersection. Upon making a request for service, the user'scomputing device can display directions to the intersection while theprovider selector 134 identifies a driver to pick up the user 210 at theintersection. In addition to identifying a driver, components of therequest processing server 130 can select a specific corner or side ofthe road near the intersection to use as the pickup location. In oneexample, the request processing server 130 selects a corner of theintersection on the driver's right side after the intersection's trafficsignal. In another example, the request processing server 130 preferswider roads for pickup locations and may therefore select a pickuplocation along an avenue instead of a side street.

FIG. 3 is a block diagram illustrating an example on-demand networkservice with predictive location selection, in accordance with examplesdescribed herein. The on-demand network platform 300 can include aprovider interface 320 to communicate over one or more networks with aservice provider application running on a service provider device.According to examples, service providers register with the on-demandnetwork platform 300 to receive service invitations through the serviceprovider application to fulfill service requests submitted by theservice requesters. In an example using transport services, the servicerequesters are prospective passengers to be picked up and transported,and the service providers are drivers who transport the servicerequesters.

Service providers can select various states or modes within the serviceprovider application, such as an online state that indicates the serviceprovider is available and willing to fulfill service invitations and atype of service offered, such as a single user transportation service ora pool transportation service where a driver transports multiple usersto their destinations simultaneously.

In accordance with various examples, the service provider devicetransmits provider data over the network to the provider interface 320.The provider data includes information such as the provider mode andservice state, the current location of the service provider, the headingof the service provider, a number of people in a vehicle with theprovider, etc. In some implementations, the service provider devices candetermine the current location of the service provider usinglocation-based resources of the service provider devices (e.g., globalpositioning system (GPS) resources). The service provider applicationcan continually update the provider status on a regular schedule or inresponse to provider input to the service provider device, locationchanges determined by GPS, service steps performed, etc. The providerinterface 320 stores the provider data in a provider datastore 340(e.g., a database or data structure) accessible by other components ofthe on-demand network platform 300 that select service areas and serviceproviders to fulfill the service requests.

The on-demand network platform 300 can include a service requesterinterface 310 to communicate with service requester devices over one ormore networks via a service requester application. In some aspects,while the service requester application is running on a servicerequester device for a user, the application transmits requester data,including the current position of the device, to the requester interface310. The service requester application can enable the service requesterto select from various service types, such as ride-sharing services thatmatch the requester with a provider who transports the requester to adestination.

In response to a selection of a particular service type and adestination, the on-demand network platform 300 can select a servicearea (i.e., a place where a driver can pick up the user in a vehicle),prior to receiving a service request from a user. The selection of theservice area can be based on the position of the user, the destination,and proximate available service providers. In another aspect, theon-demand network platform 300 can select the service area in responseto receiving the position of the user or the selection of service typewithout a destination.

The on-demand network platform 300 can provide estimated time to arrival(ETA) data on a user interface of the service requester application thatindicates the shortest ETA of nearby service providers for the servicetype and/or the locations of nearby service providers for that servicetype. As the service requester scrolls through each service type, theuser interface can update to show visual representations of the serviceproviders for that service type on a map centered on the servicerequester. The service requester can interact with the user interface ofthe service requester application to select a particular service type,input a destination, and transmit a service request.

In some examples, the on-demand network platform 300 can include amapping engine 330 to generate map data, including traffic data, in theenvironment surrounding the requester position and/or destination. Themapping engine 330 can also retrieve service provider locations, status,and availability from the provider datastore 340 or a provider trackingcomponent and include the provider locations for available providerswith the map data. In some implementations, the functionality of themapping engine 330 is provided on a separate server that the on-demandnetwork platform 300 accesses, which may be part of the network computersystem 100 or a third-party mapping service.

The map data can also include predefined service areas that are within athreshold distance or travel time from the requester position. In oneexample, the predefined service areas encompass sets of corners at roadintersections. In other examples, the predefined service areas caninclude popular service locations, such as landmarks and otherattractions. The predefined service areas can be identified or updatedfrom mapping data, manually submitted data from users, and driving dataautomatically collected by autonomous vehicles or other devices.

In some aspects, a probability scorer 350 uses the map data to determineprobability scores for each of the predefined service areas. Theprobability scorer 350 can determine the probability score for apredefined service area based on the predicted availability of each ofthe nearby providers. Determining the predicted availability of a givenprovider within a given predefined service area is based on an aggregateof factors. Among others, these factors can include travel speed for thegiven provider, distance of the given provider to the given predefinedservice area, a directional heading of the given provider, a servicedestination specified by the requester, a deviation between thedirectional heading for the given provider and a heading towards thedestination, number of people in a vehicle driven by the given provider,a prediction that the given provider receives another service requestbefore the requester submits a request for service, and historicalavailability data for the given predefined service area.

In some aspects, the probability scorer 350 can determine theprobability of a provider being available at a service area based on thetravel speed for the given provider and the distance of the givenprovider to the given predefined service area. For example, if theprovider is 200 feet from the service area and traveling at 40 feet persecond, the probability scorer 350 can determine that the provider islikely to pass the service area before the requester submits a servicerequest in an expected default timeframe (e.g., fifteen seconds). Inaddition to travel speed and distance, the probability scorer 350 canalso consider traffic conditions, including traffic signals, stop signs,etc.

In some aspects, the probability scorer 350 can determine theprobability of a provider being available at a service area based on adirectional heading of the provider, the destination specified by therequester, and a deviation between the directional heading for theprovider and a heading towards the destination.

In some aspects, the probability scorer 350 can determine theprobability of a provider being available at a service area based on anumber of people in the provider's vehicle (i.e., other passengers in aride-pooling service). For example, if the provider has a passenger inthe vehicle, there is an increased probability that the providercontinues on the current heading. Additional passengers can increasethis probability because of a higher likelihood that the passengers'destinations are closer together and located in the same generaldirection as the current heading. Therefore, for each additionalpassenger in a vehicle, the probability scorer 350 can assign higherprobabilities to the provider of being available for the requester at aservice area.

In further aspects, the probability scorer 350 can determine theprobability of a provider being available at a service area based on aprediction that the provider receives another service request before therequester submits a service request. The probability scorer 350 canretrieve supply provisioning information such as the number of localproviders, service requesters, average idle times, etc. to determine theprobability that a given provider may receive and accept a servicerequest from a different prospective passenger prior to the requestersubmitting a service request.

In further aspects, the probability scorer 350 can determine theprobability of a provider being available at a service area based onhistorical availability data around the service area. Certain areas arepoorly suited for picking up passengers due to high traffic, crowds,street layouts, etc. (e.g., popular tourist spots or subway stations).The probability scorer 350 can take advantage of historically-compiledmap data identifying which areas of a city are more challenging forproviders to pick up passengers. In one example, the map data dividesthe city into grids and assigns probability weights to each of the gridpoints based on the difficulty of using a pickup location in that area.

In one implementation, the probability scores represent independentlikelihoods of at least one service provider being available to provideservice to the requester at a service area. For example, a requester mayhave a 75% chance of getting service within a threshold period of timeat a first service area, and a 90% chance at a second service area. Inanother implementation, the probability score for a given service arearepresents the likelihood that, in response to a future service request(e.g., one made fifteen seconds after the service area is selected andtransmitted to the requester), the optimal location to fulfill thefuture service request is within the given service area.

In some aspects, the probability scorer 350 can compute multiple typesof probabilities in addition to, or instead of, the probability of arequester receiving service at a given service area. Additional typescan include the probability of a given service area yielding an optimalservice provider in terms of minimizing or maximizing wait times, traveltimes, costs, or convenience to the requester, service provider, or theon-demand network platform 300.

In one aspect, a location selection engine 355 selects the service areawith the highest probability score. In other aspects, the locationselection engine 355 calculates or receives a base score that indicatesthe quality of a match between the driver and the user. The locationselection engine 355 can then modify the base score for each serviceprovider by their respective probability score to obtain a net score. Inone aspect, the net score is the product of the base score andprobability score. The location selection engine 355 can use the netscores, rather than the base scores or probability scores alone, toselect the service area for the user. In one example, the locationselection engine 355 selects the service area corresponding to thehighest individual net score among the service providers.

Once the service area is selected, the requester interface 310 transmitsdata corresponding to the selected service area to the service requesterdevice. Upon making a request for service, the service requester devicecan display directions to the selected service area while the providerselection engine 360 identifies a provider to rendezvous with therequester in the service area to fulfill the service request at thattime.

Once the provider selection engine 360 selects a service provider and amore specific service location within the selected service area, theprovider interface 320 can transmit the service request data to theselected service provider's device. In addition to the service location,the provider interface 320 can transmit requester information, such as aname and photograph of the service requester. Upon receiving the serviceinvitation, the service provider can either accept or reject theinvitation. Rejection of the invitation can cause the provider selectionengine 360 to choose another service provider from a candidate set ofservice providers to fulfill the service request. However, if theservice provider accepts (e.g., via an acceptance input), then theacceptance input is transmitted back to the on-demand network platform300, which generates and transmits a confirmation of the serviceprovider and the service location to the service requester.

In one aspect, if the requester submits a service request before thelocation selection engine 355 has selected a service area, the mappingengine 330, probability scorer 350, and location selection engine 355can continue the process of selecting a service area and transmittingwalking directions to the service area to the requester. The providerselection engine 360 can wait until the service area is selected, or inan alternative, select a service provider concurrently with the locationselection engine determining the service area.

In one example, the requester submits the service request after theservice area is selected, but the provider selection engine 360determines that an optimal provider and/or service location is outsideof the service area. In this example, the on-demand network platform 300honors the selected service area if it has been displayed to therequester. As a result, the provider selection engine 360 selects aprovider to fulfill the service request within the selected service areaeven if other options are available.

In some aspects, the location selector 132 of FIG. 1 comprises acombination of components of the on-demand network platform 300,including the probability scorer 350, the location selection engine 355,and the mapping engine 330. Furthermore, the provider selector 134 ofFIG. 1 can comprise a combination of components including the mappingengine 330 and the provider selection engine 360.

Methodology

FIGS. 4 and 5 are flow charts describing example methods used inpredicting optimal service locations and providing the service locationsto service requesters. Although some operations of the methods aredescribed below as being performed by specific components of thecomputer systems illustrated in FIGS. 1 and 3, it will be appreciatedthat these operations need not necessarily be performed by the specificcomponents identified, and could be performed by a variety of componentsand modules, potentially distributed over a number of machines.Accordingly, references may be made to elements of the requestprocessing server 130 for the purpose of illustrating suitablecomponents or elements for performing a step or sub-step beingdescribed. Alternatively, at least certain ones of the variety ofcomponents and modules described as part of the on-demand networkplatform 300 can be arranged within a single hardware, software, orfirmware component. It will also be appreciated that some of the stepsof these methods may be performed in parallel or in a different orderthan illustrated.

FIG. 4 is a flow chart describing an example method of operating anetwork computer system with predictive location selection, according toexamples described herein. In some aspects, the network computer systemcan preselect a service location, prior to receiving a service requestfrom a user, based on the position of the user and proximate availableservice providers. In one aspect, in response to the user inputting adestination for a transportation service, the network computer systemdetermines the specific service location or a more general service area(e.g., an intersection of roads), which may be where the user has thehighest probability of matching with a service provider upon submittinga service request to the network computer system.

According to some examples, the network computer system receives datacorresponding to the position of the user from a service requesterapplication running on a computing device of the user (410). Thecomputing device can determine the position of the user throughlocation-based resources of the device (e.g., a global positioningsystem (GPS) unit). The position of the user can be in the form oflatitude and longitude coordinates, street address, or any format thatenables the network computer system to calculate distances and providedirections from the user to other locations. The service requesterapplication can continually update the position of the user on a regularschedule or in response to user input, location changes determined byGPS, service steps performed, etc.

In response to receiving the data corresponding to the position of theuser, the network computer system locates service providers based on theposition of the user (e.g., service providers within a thresholddistance or travel time from the position of the user) (420). In oneimplementation, the network computer system locates the serviceproviders in response to receiving a destination for a transportationservice from the user.

The network computer system selects predefined service areas, such assets of corners at intersections, within a threshold distance or traveltime from the position of the user (430). In some examples, the networkcomputer system can identify a region associated with the position ofthe user by comparing the position of the user to the geographical areaand/or to other data associated with the regions. Predefined serviceareas for the region are retrieved from a location datastore, database,memory, or data structure. Depending on user settings or systemdefaults, the network computer system can filter the predefined serviceareas to ones that are within a default or user-defined thresholdwalking distance, such as two city blocks, from the position of theuser.

In some aspects, the network computer system determines a probabilityscore for at least some of the predefined service areas based onpredicted availability of the service providers to provide service tothe user (440). The predicted availability is determined based on a timewhen the user is expected to request service. In some examples, thistime is predetermined, such as fifteen seconds from the current time. Inother examples, the network computer system can predict when the user isexpected to request service, which can be based on historical data forthe user and other users.

Determining the predicted availability of a given provider within agiven predefined service area is based on an aggregate of factors. Amongothers, these factors can include travel speed for the given provider,distance of the given provider to the given predefined service area, adirectional heading of the given provider, a service destinationspecified by the user, a deviation between the directional heading forthe given provider and a heading towards the service destination, numberof people in a vehicle driven by the given provider, a prediction thatthe given provider receives another service request before the userrequests service, and historical availability data for the givenpredefined service area.

In some aspects, the network computer system selects one of thepredefined service areas based on the probability scores (450). In oneexample, the network computer system selects the predefined service areawith the highest probability score, which represents the area where theuser is more likely to successfully receive service from a serviceprovider, in comparison to the other predefined service areas.

Once the service area is selected, the network computer systemtransmits, to the computing device of the user, data corresponding tothe selected service area (460). The data can include a map location forthe selected service area and walking directions from the position ofthe user to the selected service area. Therefore, once the user makes arequest for service with the computing device, the device can displaythe map location and walking directions to the selected service areawithout waiting to receive confirmation of a service provider.

FIG. 5 is a flow chart describing an example method of predictivelocation selection, according to examples described herein. Rather thanrequiring a user to manually choose a location to receive a service(e.g., a pickup location for a ride-sharing service), a network computersystem can determine the location, or service area, where the user hasthe highest probability of matching with a service provider uponsubmitting a service request to the network computer system.

In some examples, the network computer system receives datacorresponding to the position of the user and a service destination fora transportation service from an application running on a computingdevice of the user (510). In response to receiving the destination, thenetwork computer system locates service providers and predefined serviceareas based on the position of the user (e.g., service providers andareas within a threshold distance or travel time from the position ofthe user) and the destination.

For each of the nearby providers, the network computer system determinesa probability of the provider being available at each of the predefinedservice areas at a time when the user is expected to request service.The network computer system can base the probability of a provider beingavailable at a service area on a number of factors including the travelspeed for the provider, distance of the provider to the service area, adirectional heading of the provider, the destination specified by theuser, a deviation between the directional heading for the provider and aheading towards the destination, a number of people in the provider'svehicle (i.e., other passengers in a ride-pooling service), a predictionthat the provider receives another service request before the userrequests service, and historical availability data for a region aroundthe service area.

In one aspect, each of the factors represents a rule that the networkcomputer system uses to determine whether the provider is available at aservice area. If the provider data for a given provider meets each ofthe rules within a threshold tolerance for a given service area, thatprovider is deemed available at that service area. In other aspects, thenetwork computer system can use a weighted ranking method to assignprobabilities for each provider at each service area.

In one example, once the network computer system has determinedprobabilities for each of the nearby providers, the network computersystem can aggregate the provider probabilities to calculate aprobability score for each of the predefined service areas.Consequently, service areas with more nearby providers who have higherprobabilities of being available at that service area have a higherprobability score than service areas with fewer, less likely providers.

In one implementation, the probability scores represent independentlikelihoods of at least one service provider being available to provideservice to the user at a service area. For example, a user may have a75% chance of getting service within a threshold period of time at afirst service area, and a 90% chance at a second service area. Inanother implementation, the probability score for a given service arearepresents the likelihood that, in response to a future service request(e.g., one made fifteen seconds after the service area is selected andtransmitted to the user), the optimal location to fulfill the futureservice request is within the given service area. In bothimplementations, the network computer system can select the service areawith the highest probability score.

In another example, the network computer system calculates a base scorefor each of the service providers that indicates the quality of a matchbetween the service provider and the user. The network computer systemcan then modify the base score for each provider by their respectiveprobability score to obtain a net score. In one aspect, the net score isthe product of the base score and probability score. The networkcomputer system can use the net scores, rather than the base scores orprobability scores alone, to select the service area for the user. Inone example, the network computer system selects the service areacorresponding to the highest individual net score among the serviceproviders.

Once the service area is selected, the network computer systemtransmits, to the computing device of the user, data corresponding tothe selected service area (530). The data can include a map location forthe selected service area and walking directions from the position ofthe user to the selected service area. Therefore, once the user makes arequest for service with the computing device, the device can displaythe map location and walking directions to the selected service areawithout waiting to receive confirmation of a service provider.

However, if the user does not submit a service request within athreshold period of time (e.g., 30 seconds), the user's computing devicecan request an updated service area from the network computer system,thereby preventing the preselected service area from going stale (535).If the programmed period of time passes, the user's computing device canprovide the network computer system with the location of the user andany selected destination and request an updated service area. Thenetwork computer system finds predefined service areas near the locationof the user, recalculates probability scores for each of the serviceareas, and transmits updated walking directions to one of the serviceareas selected based on the probability scores.

If the user submits a request for service (540) before the thresholdperiod of time expires, the user's computing device can display thewalking directions to the selected service area (550). As the user walkstowards the selected service area, components of the network computersystem perform a provider selection process to identify a serviceprovider to fulfill the service request at a point, or service location,within the selected service area (560). For example, if the service areais a road intersection in a city, the service location can be one of thecorners of the intersection or a point along the road after theintersection. Once the network computer system selects and receivesconfirmation from a service provider, the network computer systemtransmits the service location and service provider data to be displayedon the user's computing device. The user's computing device can updatethe walking directions to guide the user to the service location, andthe device can also display data including an estimated time to arrivalfor the service provider, a picture of the provider, license platenumber, rating, etc.

Service Requester Device

FIG. 6 is a block diagram illustrating an example service requesterdevice executing a designated service requester application for anon-demand service, as described herein. In many implementations, theservice requester device 680 can comprise a mobile computing device,such as a smartphone, tablet computer, laptop computer, VR or AR headsetdevice, and the like. As such, the service requester device 680 caninclude typical telephony features such as a microphone 645, a camera650, and a communication interface 610 to communicate with externalentities using any number of wireless communication protocols. Incertain aspects, the service requester device 680 can store a designatedapplication (e.g., a service requester application 632) in a localmemory 630. In many aspects, the service requester device 680 furtherstores information corresponding to a contacts list 634 and calendarappointments 636 in the local memory 630. In variations, the memory 630can store additional applications executable by one or more processors640 of the service requester device 680, enabling access and interactionwith one or more host servers over one or more networks 660.

In response to user input, a processor 640 can execute the servicerequester application 632, which can cause an application interface tobe generated on a display screen 620 of the service requester device680. The application interface can enable the service requester to, forexample, check current price levels and availability for the on-demandarrangement service. In various implementations, the applicationinterface can further enable the service requester to select frommultiple ride service types, such as a carpooling service type, aregular ride-sharing service type, a professional ride service type, avan on-demand service type, a luxurious ride service type, and the like.In addition, the application interface can enable the service requesterto input a desired destination for the type of service selected.

As provided herein, the service requester application 632 can furtherenable a communication link with a network computer system 600 over thenetwork 660, such as the network computer system 100 as shown anddescribed with respect to FIG. 1. Furthermore, as discussed herein, theservice requester application 632 can display walking directions to aservice area selected by the network computer system 600 on theapplication interface. In addition, the application interface candisplay information regarding a selected service provider and anyservice request, including a service location, estimated time to arrivalor destination, a picture of the service provider, license plate number,rating, vehicle type, and the like.

In various examples, the service requester device 680 can furtherinclude a GPS module 655, which can provide location data indicating thecurrent location of the requester to the network computer system 600 sothat the system can select an appropriate service area and servicerequester to fulfill user service requests.

In alternative aspects, hard-wired circuitry may be used in place of, orin combination with, software instructions to implement aspectsdescribed herein. Thus, aspects described are not limited to anyspecific combination of hardware circuitry and software configuration.

Hardware Diagram

FIG. 7 is a block diagram that illustrates a computer system upon whichexamples described herein may be implemented. A computer system 700 canrepresent, for example, hardware for a server or combination of serversthat may be implemented as part of a network service for providingon-demand services. In the context of FIG. 1, the network computersystem 100 may be implemented using a computer system 700 or combinationof multiple computer systems 700 as described by FIG. 7.

In one aspect, the computer system 700 includes processing resources(processor 710), a main memory 720, a read-only memory (ROM) 730, astorage device 740, and a communication interface 750. The computersystem 700 includes at least one processor 710 for processinginformation stored in the main memory 720, such as provided by a randomaccess memory (RAM) or other dynamic storage device, for storinginformation and instructions which are executable by the processor 710.The main memory 720 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by the processor 710. The computer system 700 may also includethe ROM 730 or other static storage device for storing staticinformation and instructions for the processor 710. A storage device740, such as a magnetic disk or optical disk, is provided for storinginformation and instructions.

The communication interface 750 enables the computer system 700 tocommunicate with one or more networks (e.g., a cellular network) throughuse of a network link (wireless or wired). Using the network link, thecomputer system 700 can communicate with one or more computing devices,one or more servers, and/or one or more self-driving vehicles. Inaccordance with some examples, the computer system 700 receives servicerequests from mobile computing devices of individual users. Theexecutable instructions stored in the memory 730 can include servicearea selection instructions 724 and provider selection instructions 726to perform one or more of the methods described herein when executed.

By way of example, the instructions and data stored in the memory 720can be executed by the processor 710 to implement an example requestprocessing server 130 of FIG. 1. In performing the operations, theprocessor 710 can handle service requests and provider statuses andsubmit service invitations to facilitate fulfilling the servicerequests. The processor 710 executes instructions for the softwareand/or other logic to perform one or more processes, steps and otherfunctions described with implementations, such as described by FIGS. 1through 6.

Examples described herein are related to the use of the computer system700 for implementing the techniques described herein. According to oneexample, those techniques are performed by the computer system 700 inresponse to the processor 710 executing one or more sequences of one ormore instructions contained in the main memory 720. Such instructionsmay be read into the main memory 720 from another machine-readablemedium, such as the storage device 740. Execution of the sequences ofinstructions contained in the main memory 720 causes the processor 710to perform the process steps described herein. In alternativeimplementations, hard-wired circuitry may be used in place of or incombination with software instructions to implement examples describedherein. Thus, the examples described are not limited to any specificcombination of hardware circuitry and software.

It is contemplated for examples described herein to extend to individualelements and concepts described, independently of other concepts, ideasor systems, as well as for examples to include combinations of elementsrecited anywhere in this application. Although examples are described indetail herein with reference to the accompanying drawings, it is to beunderstood that the concepts are not limited to those precise examples.As such, many modifications and variations will be apparent topractitioners skilled in this art. Accordingly, it is intended that thescope of the concepts be defined by the following claims and theirequivalents. Furthermore, it is contemplated that a particular featuredescribed either individually or as part of an example can be combinedwith other individually described features, or parts of other examples,even if the other features and examples make no mention of theparticular feature. Thus, the absence of describing combinations shouldnot preclude claiming rights to such combinations.

What is claimed is:
 1. A method for operating a network computer system,the method being performed by one or more processors of the networkcomputer system and comprising: receiving, from a computing device of auser over a network, data corresponding to a position of the user at afirst time; based on the position of the user, selecting a plurality ofproviders and a plurality of predefined service areas; generatingprobability scores for at least some of the predefined service areas,the probability scores based on predicted availability of the pluralityof providers to provide service to the user within that predefinedservice area at a second time, wherein determining the predictedavailability of a given provider within a given predefined service areaat the second time is based, at least in part, on a comparison of adirectional heading of the given provider and a service destinationspecified by the user; determining an optimal service area, from theplurality of predefined service areas, based on the probability scores;and transmitting, to the computing device of the user, datacorresponding to the optimal service area.
 2. The method of claim 1,wherein the second time corresponds to a predicted delay between thefirst time and receiving a request for service from the user.
 3. Themethod of claim 2, wherein determining the predicted availability of thegiven provider within a given predefined service area at the second timeis based on an aggregate of factors including one or more of: travelspeed for the given provider, distance of the given provider to thegiven predefined service area, backtracking distance of the givenprovider to the given predefined service area, traffic conditions, theservice destination specified by the user, a deviation between thedirectional heading for the given provider and a heading towards theservice destination, number of people in a vehicle driven by the givenprovider, a prediction that the given provider receives another servicerequest before the second time, and historical availability data for thegiven predefined service area.
 4. The method of claim 2, furthercomprising: receiving, over the network, data corresponding to therequest for service from the computing device of the user at the secondtime; and selecting an available provider to provide service for theuser at a point within the optimal service area.
 5. The method of claim4, wherein the data corresponding to the optimal service area istransmitted, to the computing device of the user, to be displayed on thecomputing device in response to the user submitting the request forservice.
 6. The method of claim 4, wherein the data corresponding to theoptimal service area includes walking directions from the position ofthe user to the optimal service area.
 7. The method of claim 4, furthercomprising: in response to selecting the available provider,transmitting, to the computing device of the user to be displayed on thecomputing device, data corresponding to the available provider and thepoint within the optimal service area.
 8. The method of claim 1, whereinthe optimal service area has the highest probability score among thepredefined service areas.
 9. The method of claim 1, wherein thepredefined service areas are road intersections.
 10. The method of claim1, wherein the probability scores are further based on predictedavailability of the plurality of providers to provide service to aplurality of service requesters including the user.
 11. A networkcomputer system comprising: one or more processors; and one or morememory resources storing instructions that, when executed by the one ormore processors, cause the network computer system to: receive, from acomputing device of a user over a network, data corresponding to aposition of the user at a first time; based on the position of the user,select a plurality of providers and a plurality of predefined serviceareas; generate probability scores for at least some of the predefinedservice areas, the probability scores based on predicted availability ofthe plurality of providers to provide service to the user within thatpredefined service area at a second time, wherein determining thepredicted availability of a given provider within a given predefinedservice area at the second time is based, at least in part, on acomparison of a directional heading of the given provider and a servicedestination specified by the user; determine an optimal service area,from the plurality of predefined service areas, based on the probabilityscores; and transmit, to the computing device of the user, datacorresponding to the optimal service area.
 12. The network computersystem of claim 11, wherein the second time corresponds to a predicteddelay between the first time and receiving a request for service fromthe user.
 13. The network computer system of claim 12, whereindetermining the predicted availability of the given provider within agiven predefined service area at the second time is based on anaggregate of factors including one or more of: travel speed for thegiven provider, distance of the given provider to the given predefinedservice area, backtracking distance of the given provider to the givenpredefined service area, traffic conditions, the service destinationspecified by the user, a deviation between the directional heading forthe given provider and a heading towards the service destination, numberof people in a vehicle driven by the given provider, a prediction thatthe given provider receives another service request before the secondtime, and historical availability data for the given predefined servicearea.
 14. The network computer system of claim 12, further comprising:receiving, over the network, data corresponding to the request forservice from the computing device of the user at the second time; andselecting an available provider to provide service for the user at apoint within the optimal service area.
 15. The network computer systemof claim 14, wherein the data corresponding to the optimal service areais transmitted, to the computing device of the user, to be displayed onthe computing device in response to the user submitting the request forservice.
 16. The network computer system of claim 14, wherein the datacorresponding to the optimal service area includes walking directionsfrom the position of the user to the optimal service area.
 17. Thenetwork computer system of claim 14, further comprising: in response toselecting the available provider, transmitting, to the computing deviceof the user to be displayed on the computing device, data correspondingto the available provider and the point within the optimal service area.18. The network computer system of claim 11, wherein the optimal servicearea has the highest probability score among the predefined serviceareas.
 19. The network computer system of claim 11, wherein thepredefined service areas are road intersections.
 20. A non-transitorycomputer-readable medium that stores instructions, executable by one ormore processors, to cause the one or more processors to performoperations including: receiving, from a computing device of a user overa network, data corresponding to a position of the user at a first time;based on the position of the user, selecting a plurality of providersand a plurality of predefined service areas; generating probabilityscores for at least some of the predefined service areas, theprobability scores based on predicted availability of the plurality ofproviders to provide service to the user within that predefined servicearea at a second time, wherein determining the predicted availability ofa given provider within a given predefined service area at the secondtime is based, at least in part, on a comparison of a directionalheading of the given provider and a service destination specified by theuser; determining an optimal service area, from the plurality ofpredefined service areas, based on the probability scores; andtransmitting, to the computing device of the user, data corresponding tothe optimal service area.